A Data Science Approach to Organic Reaction Discovery and Development

反应开发的重点是确定促进原料转化为所需产品的最佳条件。将密切相关反应（例如涉及相似底物的反应）的条件应用于目标转化在很大程度上推动了当前方法开发的过程。不幸的是，由于反应需求的细微差异，这种方法经常失败。因此，优化过程仍然是一个经验的努力。我们的项目计划通过开发能够预测和解释反应结果的新数据科学方法来应对这一挑战。这项工作将对化学合成产生三个关键影响：1)简化昂贵的反应优化过程，2)使反应应用于复杂的底物。3)由于数据驱动工具利用物理有机方法在数学上定义分子，由此产生的相关性可以解释为了解催化剂/底物如何相互作用。在我们的方法中，描述反应组分结构特征的dft衍生参数集与实验输出相关。当这些成分发生变化时，所得到的方程可以用来预测实验结果。所研究的反应和催化剂将在结构和应用方面有很大的不同，但总体目标是了解有效催化的相互作用，并开发新的数据驱动工具，以促进反应设计。这一建议将由两个关键的新思想的调查推动。首先是利用增强的反应筛选预测工具构建数据科学工作流程，以便快速确定催化的最佳条件。第二个问题是关于在机械不同的反应中决定相互作用的选择性的普遍性。对于类似的催化剂或底物类型，控制对映体选择性结果的非共价相互作用是否基本相同？回答这个重要的问题将对反应建模产生长远的影响，并使我们能够解决以下具体问题：1)模型可以外推多远的“样本外”？2)不同的催化剂化学型如何包含在同一相关性中？3)可以建立一般的机制模型吗？综上所述，我们提出了一项研究计划，旨在开发和推进预测策略，以解决有机合成中的关键问题。最终，这些工具将增强我们将已知反应应用于复杂目标和开发新方法的能力，立即影响人们在实验室中进行实验的方式。这项研究将为下一代现代有机化学家提供独特的培训机会，并将通过开发可在工业环境中实施的简化反应优化的新方法，为加拿大人带来好处。

Reaction development is focused on the identification of optimal conditions that facilitates the conversion of starting materials to a desired product. The application of conditions from closely-related reactions (e.g. those involving similar substrates) to the target transformation largely drives the current process of method development. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Therefore, the optimization process continues to be an empirical endeavor. Our program plans to address this challenge by developing new data science approaches capable of predicting and interpreting reaction outcomes. This work would have three key impacts in chemical synthesis: 1) streamlining the costly process of reaction optimization, 2) enable reaction application to complex substrates. And 3) as the data driven tools utilize physical organic methods to define molecules mathematically, the resulting correlation can be interpreted to provide insights into how catalysts/substrates interact. In our approach, DFT-derived parameter sets describing the structural features of the reaction components are related to experimental outputs. The resulting equation can be deployed to predict experimental outcomes when these components are altered. The reactions and catalysts under study will range significantly in structure and application, but the general goal is to understand the interactions responsible for effective catalysis and to develop new data-driven tools that will facilitate reaction design. This proposal will be driven by the investigation of two critical new ideas. The first is to build data science workflows with enhanced prediction tools for reaction screening to enable rapid identification of optimal conditions for catalysis. The second asks questions pertaining to the generality of selectivity determining interactions in mechanistically disparate reactions. Are the non-covalent interactions governing the enantioselectivity outcomes with similar catalyst or substrate types fundamentally the same? Answering this important question will have long-ranging implications on reaction modeling and allow us to solve the following specific problems: 1) How far "out-of-sample" can the model(s) extrapolate? 2) How can different catalyst chemotypes be included in the same correlation? 3) Can general mechanistic models be developed? In sum, we propose a research program that aims to develop and advance predictive strategies to solve key problems in organic synthesis. Ultimately, such tools will enhance our ability to apply known reactions to complex targets and develop new methods, immediately impacting how one performs experiments in the laboratory. This research will provide unique training opportunities for the next generation of modern organic chemists and will lead to benefits for Canadians by developing new methods for streamlining the reaction optimization that could be implemented in industrial settings.